Detecting Metabolic Thresholds from Nonlinear Analysis of Heart Rate Time Series: A Review

Giovanna Zimatore (), Maria Chiara Gallotta, Matteo Campanella, Piotr H. Skarzynski, Giuseppe Maulucci (), Cassandra Serantoni, Marco De Spirito, Davide Curzi, Laura Guidetti, Carlo Baldari and Stavros Hatzopoulos
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Giovanna Zimatore: Department of Theoretical and Applied Sciences, eCampus University, 22060 Novedrate, Italy
Maria Chiara Gallotta: Department of Physiology and Pharmacology “Vittorio Erspamer”, Sapienza University of Rome, 00185 Roma, Italy
Matteo Campanella: Department of Theoretical and Applied Sciences, eCampus University, 22060 Novedrate, Italy
Piotr H. Skarzynski: Department of Teleaudiology and Screening, World Hearing Center, Institute of Physiology and Pathology of Hearing, 02-042 Warsaw, Poland
Giuseppe Maulucci: Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy
Cassandra Serantoni: Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy
Marco De Spirito: Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy
Davide Curzi: Department Unicusano, Niccolò Cusano University, 00166 Rome, Italy
Laura Guidetti: Department Unicusano, Niccolò Cusano University, 00166 Rome, Italy
Carlo Baldari: Department of Theoretical and Applied Sciences, eCampus University, 22060 Novedrate, Italy
Stavros Hatzopoulos: Clinic of Audiology & ENT, University of Ferrara, 44121 Ferrara, Italy

IJERPH, 2022, vol. 19, issue 19, 1-24

Abstract: Heart rate time series are widely used to characterize physiological states and athletic performance. Among the main indicators of metabolic and physiological states, the detection of metabolic thresholds is an important tool in establishing training protocols in both sport and clinical fields. This paper reviews the most common methods, applied to heart rate (HR) time series, aiming to detect metabolic thresholds. These methodologies have been largely used to assess energy metabolism and to identify the appropriate intensity of physical exercise which can reduce body weight and improve physical fitness. Specifically, we focused on the main nonlinear signal evaluation methods using HR to identify metabolic thresholds with the purpose of identifying a method which can represent a useful tool for the real-time settings of wearable devices in sport activities. While the advantages and disadvantages of each method, and the possible applications, are presented, this review confirms that the nonlinear analysis of HR time series represents a solid, robust and noninvasive approach to assess metabolic thresholds.

Keywords: metabolic threshold; heart rate variability; sport; recurrence quantification analysis; nonlinear dynamic; Poincaré plot; wearable devices (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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